Person-centered approaches for dating violence among adolescents and young adults: A systematic review
Bibliographic record
Abstract
An increasing number of studies have documented the heterogeneous nature of dating violence among youth using person-centered approaches. These studies have identified distinct subgroups of individuals, who share similarities in their experiences with different forms of dating violence. Identifying dating violence classes is an essential first step for informing prevention and to guide future research. The current systematic review aimed to examine and synthesize the literature on person-centered approaches on dating violence among adolescents and young adults as well as to identify sex or gender differences. Five databases were search to identify peer-reviewed studies (2000−2022) and theses (2015–2022) written in French, English, or Spanish, with participants aged 11 to 25. Only studies using latent class or latent profile analyses on dating violence victimization and/or perpetration (psychological, physical, and/or sexual) with at least two forms of violence were included. Drawn from 22 studies, findings highlight four typical classes of dating violence: a little or no involvement class (the largest class), a psychological violence class (typically the second largest class), a psychological and physical violence class and a multiple forms of violence class. Results on sex or gender differences are mixed. Similar numbers of classes were generally identified for boys and girls, although two studies found a greater number of classes for girls. Variations in the operationalization of dating violence limit result comparability. Nonetheless, results highlight that prevention should focus on psychological violence, as nearly all individuals involved in dating violence are involved in this form of violence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".